Gender Differences in Trends in Incidence and Mortality of Acute Myocardial Infarction in the Small Island Developing State of Barbados
Bibliographic record
Abstract
Objective To determine trends, identify predictors of acute myocardial infarction (AMI) incidence and mortality, and explore performance metrics for AMI care in Barbados. Methods Data on all cases diagnosed with AMI were collected by the Barbados National Registry for Non-Communicable Diseases (BNR) from the island's only tertiary hospital, the Queen Elizabeth Hospital, and the National Vital Registration Department. Participants who survived hospital admission were then followed up at 28 days and one year post event via telephone survey and retrieval of death certificates. Age-standardized incidence and mortality rates were calculated. Determinants of mortality at 28 days were examined in multivariable logistic regression models. Median and interquartile ranges (IQR) were calculated for performance metrics (e.g., time from pain onset to reperfusion). Results In a 10-year period between 2010 and 2019, 4,065 cases of myocardial infarction were recorded. The median age of the sample was 73 years (IQR: 61,83), and approximately half (47%) were female. Over a 10-year period, standardized incidence increased in women on average yearly by three per 100,000 (95% CI: 1 to 6; p=0.02), while in men, the average increase per year was six per 100,000 (95% CI: 4 to 8; p<0.001). There was no increase in 28-day mortality in women; mortality in men increased each year by 2.5 per 100,000 (95% CI: 0.4 to 4.5; p=0.02). The time from arrival at the hospital to the ECG was 44 minutes IQR (20,113). Conclusion AMI incidence and mortality are increasing in Barbados, and men have a higher velocity of mortality rate increase than women, which contradicts global data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".